National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach
Understanding the spatial variation of soil pH is critical for many different stakeholders across different fields of science, because it is a master variable that plays a central role in many soil processes. This study documents the first attempt to map soil pH (1:5 H<inline-formula><math...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-09-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/18/2872 |
| _version_ | 1797554569358606336 |
|---|---|
| author | Pierre Roudier Olivia R. Burge Sarah J. Richardson James K. McCarthy Gerard J. Grealish Anne-Gaelle Ausseil |
| author_facet | Pierre Roudier Olivia R. Burge Sarah J. Richardson James K. McCarthy Gerard J. Grealish Anne-Gaelle Ausseil |
| author_sort | Pierre Roudier |
| collection | DOAJ |
| description | Understanding the spatial variation of soil pH is critical for many different stakeholders across different fields of science, because it is a master variable that plays a central role in many soil processes. This study documents the first attempt to map soil pH (1:5 H<inline-formula><math display="inline"><semantics><msub><mrow/><mn>2</mn></msub></semantics></math></inline-formula>O) at high resolution (100 m) in New Zealand. The regression framework used follows the paradigm of digital soil mapping, and a limited number of environmental covariates were selected using variable selection, before calibration of a quantile regression forest model. In order to adapt the outcomes of this work to a wide range of different depth supports, a new approach, which includes depth of sampling as a covariate, is proposed. It relies on data augmentation, a process where virtual observations are drawn from statistical populations constructed using the observed data, based on the top and bottom depth of sampling, and including the uncertainty surrounding the soil pH measurement. A single model can then be calibrated and deployed to estimate pH a various depths. Results showed that the data augmentation routine had a beneficial effect on prediction uncertainties, in particular when reference measurement uncertainties are taken into account. Further testing found that the optimal rate of augmentation for this dataset was 3-fold. Inspection of the final model revealed that the most important variables for predicting soil pH distribution in New Zealand were related to land cover and climate, in particular to soil water balance. The evaluation of this approach on those validation sites set aside before modelling showed very good results (<inline-formula><math display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.65</mn></mrow></semantics></math></inline-formula>, <inline-formula><math display="inline"><semantics><mrow><mi>C</mi><mi>C</mi><mi>C</mi><mo>=</mo><mn>0.79</mn></mrow></semantics></math></inline-formula>, <inline-formula><math display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi><mo>=</mo><mn>0.54</mn></mrow></semantics></math></inline-formula>), that significantly out-performed existing soil pH information for the country. |
| first_indexed | 2024-03-10T16:34:02Z |
| format | Article |
| id | doaj.art-80271f7fd50845edaa6bf198d74790ab |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T16:34:02Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-80271f7fd50845edaa6bf198d74790ab2023-11-20T12:37:35ZengMDPI AGRemote Sensing2072-42922020-09-011218287210.3390/rs12182872National Scale 3D Mapping of Soil pH Using a Data Augmentation ApproachPierre Roudier0Olivia R. Burge1Sarah J. Richardson2James K. McCarthy3Gerard J. Grealish4Anne-Gaelle Ausseil5Manaaki Whenua—Landcare Research, Private Bag 11052, Manawatū Mail Centre, Palmerston North 4442, New ZealandManaaki Whenua—Landcare Research, P.O. Box 69040, Lincoln 7640, New ZealandManaaki Whenua—Landcare Research, P.O. Box 69040, Lincoln 7640, New ZealandManaaki Whenua—Landcare Research, P.O. Box 69040, Lincoln 7640, New ZealandManaaki Whenua—Landcare Research, Private Bag 11052, Manawatū Mail Centre, Palmerston North 4442, New ZealandManaaki Whenua—Landcare Research, P.O. Box 10, Wellington 6143, New ZealandUnderstanding the spatial variation of soil pH is critical for many different stakeholders across different fields of science, because it is a master variable that plays a central role in many soil processes. This study documents the first attempt to map soil pH (1:5 H<inline-formula><math display="inline"><semantics><msub><mrow/><mn>2</mn></msub></semantics></math></inline-formula>O) at high resolution (100 m) in New Zealand. The regression framework used follows the paradigm of digital soil mapping, and a limited number of environmental covariates were selected using variable selection, before calibration of a quantile regression forest model. In order to adapt the outcomes of this work to a wide range of different depth supports, a new approach, which includes depth of sampling as a covariate, is proposed. It relies on data augmentation, a process where virtual observations are drawn from statistical populations constructed using the observed data, based on the top and bottom depth of sampling, and including the uncertainty surrounding the soil pH measurement. A single model can then be calibrated and deployed to estimate pH a various depths. Results showed that the data augmentation routine had a beneficial effect on prediction uncertainties, in particular when reference measurement uncertainties are taken into account. Further testing found that the optimal rate of augmentation for this dataset was 3-fold. Inspection of the final model revealed that the most important variables for predicting soil pH distribution in New Zealand were related to land cover and climate, in particular to soil water balance. The evaluation of this approach on those validation sites set aside before modelling showed very good results (<inline-formula><math display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.65</mn></mrow></semantics></math></inline-formula>, <inline-formula><math display="inline"><semantics><mrow><mi>C</mi><mi>C</mi><mi>C</mi><mo>=</mo><mn>0.79</mn></mrow></semantics></math></inline-formula>, <inline-formula><math display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi><mo>=</mo><mn>0.54</mn></mrow></semantics></math></inline-formula>), that significantly out-performed existing soil pH information for the country.https://www.mdpi.com/2072-4292/12/18/2872digital soil mappingsoil pHdata augmentationquantile regression forest |
| spellingShingle | Pierre Roudier Olivia R. Burge Sarah J. Richardson James K. McCarthy Gerard J. Grealish Anne-Gaelle Ausseil National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach Remote Sensing digital soil mapping soil pH data augmentation quantile regression forest |
| title | National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach |
| title_full | National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach |
| title_fullStr | National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach |
| title_full_unstemmed | National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach |
| title_short | National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach |
| title_sort | national scale 3d mapping of soil ph using a data augmentation approach |
| topic | digital soil mapping soil pH data augmentation quantile regression forest |
| url | https://www.mdpi.com/2072-4292/12/18/2872 |
| work_keys_str_mv | AT pierreroudier nationalscale3dmappingofsoilphusingadataaugmentationapproach AT oliviarburge nationalscale3dmappingofsoilphusingadataaugmentationapproach AT sarahjrichardson nationalscale3dmappingofsoilphusingadataaugmentationapproach AT jameskmccarthy nationalscale3dmappingofsoilphusingadataaugmentationapproach AT gerardjgrealish nationalscale3dmappingofsoilphusingadataaugmentationapproach AT annegaelleausseil nationalscale3dmappingofsoilphusingadataaugmentationapproach |